Predictive maintenance cuts costs and gives companies asset control. But how does it work, and what does it require? Can you count on a return on your investment? Yes, you can, and here is all you need to know about predictive maintenance!
The idea of predictive maintenance originated during WW2, when C.H. Waddington, an advisor to the British military – proposed a new maintenance planning regime. Back then, it was called «condition-based maintenance», and it improved British fighter planes’ performance like never before.
Condition-based maintenance (CBM) was soon adapted by commercial industries in the late 40s and further developed until the 70s. In the 80s and 90s, digitalized CBM driven by CMMS and EAM software quickly caught on before turning to the IoT predictive maintenance we know today.
Predictive maintenance definition
Predictive maintenance is the investigation of systems and processes to plan and engage in upkeep as cost-effectively, reliably, and efficiently as possible. With systematic monitoring, this method enables companies to plan maintenance with minimal or no production interruptions.
The idea is to analyze real-time data and only implement maintenance when necessary rather than engage in routine upkeep to prevent production standstills. The process relies on frequent monitoring, data collection, and information investigation.
Preventive vs. predictive maintenance
Preventive maintenance means upkeep on a regular basis. Predictive maintenance entails constant monitoring and upkeep when needed. Both types of maintenance prevent unexpected production standstills. However, Predictive maintenance is designed to eliminate the unnecessary risks and resources spent on preventive maintenance.
In other words, preventive maintenance does not account for the equipment’s status. It is performed on schedule, even though systems and machines might run safely for much longer. This costs money and introduces excessive maintenance wear and personnel risks. Predictive maintenance means constant equipment monitoring and ensuring that all systems and machines are used to the maximum of their capacity.
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What is required for predictive maintenance?
The concept of predictive maintenance is clear, but every company must adapt the method to their needs. Depending on the line of business, variables and tools differ. However, some standard components are crucial for getting started:
1. Data collection and preprocessing
To facilitate solid predictive maintenance, real-time data collecting and preprocessing are vital. Predicting system/process durability requires much data, which often are preprocessed so that only quality data is extrapolated for analysis.
2. Fault detection and isolation
Fault detection and isolation identify faults, pinpoint where they happened, and classify what kind of fault. This vital part of predictive maintenance aims at decreasing unforeseen system occurrences.
3. Time to failure calculation
Analysis of condition and prediction of future failure, often referred to as RUL (rest useful lifetime), is a crucial feature of predictive maintenance. The idea is to use historical data, real-time data, and anomaly detectors to find and alert maintenance, upgrade or repair needs before they occur.
4. Maintenance plan
A maintenance plan optimizes your resource consumption by scheduling maintenance only when needed. The plan updates if anomalies are detected, and maintenance is strategically planned during natural production halts, such as after hours, on weekends, or at night.
5. Predictive maintenance tools
To get started, you need one or several tools. For instance, wireless sensors and gateways are standard equipment that gives you access to real-time data on your assets.
6. Predictive maintenance software
Collection, structuring, and analysis of data are vital for efficient predictive maintenance. IoT software and apps ensure that sensor readings are accurately processed and provide status updates at all times.
Examples of predictive maintenance
In the late 40s, when predictive maintenance was still called condition-based maintenance, American railway companies were among the first to commercialize the concept. They used it to detect fuel and coolant leaks in the engine’s lubricating oil.
Today, predictive maintenance is still widespread among railway companies worldwide. For instance, it assesses warning signs of equipment fatigue and hinders train standstills.
Similarly, predictive maintenance is applied in many manufacturing industries to ensure production optimization. El-Watch has provided predictive maintenance systems for meat producers, lumber producers, restaurants, and farms, to mention a few.
Related article: Sensors prevented machine breakdown at large lumber producer
Offshore drilling companies also rely greatly on predictive maintenance since much of their underwater equipment lacks visibility. These companies get reliable predictions on system and equipment lifetimes by monitoring and collecting big data.
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How IoT improves predictive maintenance
The first and foremost benefit of combining predictive maintenance with IoT is that anyone with access can monitor systems remotely. You can check the system status from your smartphone or laptop and immediately get notified about irregularities.
IoT-driven maintenance reduces costs, shortens time-to-action, promotes manufacturing safety, and makes industries ready for automating larger parts of their operations. It connects to the so-called «IIoT», also known as the Industrial Internet of Things.
The quick rise of IoT in the 2010s brought along the concept of smart homes. Lately, the idea has expanded to include smart cities, and in the industry, we are now preparing for smart factories.
Automation and predictive maintenance are two cornerstones of smart factories, neither of which would be possible without IoT. It makes management easier, communication effortless, and system analytics available to anyone with access to the company platform.
Is predictive maintenance a good investment?
In March 2022, Bloomberg put out a report estimating that the predictive maintenance market would be worth $15.9 billion by 2026. It is not without reason that this industry is expected to grow by 30.6 percent in four years.
According to IBM’s trusted advisory group, ARC, as much as 50 percent of investments in preventive maintenance are wasted. Correspondingly, a study by McKinsey said that predictive maintenance reduced costs by 18-25 percent and increased asset availability by 5–15 percent.
A reason for this is that overzealous reactive and preventive maintenance wears out machinery and equipment. Every time maintenance is performed, it also causes wear and tear. Predictive maintenance minimizes such equipment deterioration.
Moreover, a report from a PWC study stated that the lifecycle of aging assets increased by 20 percent with predictive maintenance. The same report found that it also reduces safety, health, environment, and quality risks by 14 percent.
Maintenance can be a hazardous job. It is often performed under heavy time restraints with reduced safety barriers. As a result, the frequency of accidents and casualties is higher than in normal operations.
In France, 14 percent of fatal accidents are related to the maintenance of machines, devices, and equipment. Hence, a reduction in time spent on maintenance equals reduced accidents and less time spent in hazardous environments.
A report from CXG Group said that 91 percent of companies implementing predictive maintenance saw a decrease in repair times and unplanned downtimes. Ninety-three percent experienced improvements in aging industrial infrastructure.
We are not arguing that switching to predictive maintenance comes without challenges. Yet, the numbers in the above reports indicate that it is a good investment for companies that make the switch successfully.
El-Watch predictive maintenance solutions
El-Watch specializes in IoT monitoring with Neuron sensors. We offer complete solutions, including a wide range of wireless sensors, gateways, cloud computing, and our own app for a total overview and insight into your equipment.
We also focus strongly on predictive maintenance and how our solutions promote it in various industries. For this purpose, we take pride in providing robust solutions that are easy to install, straightforward to use, and require little to no upkeep.
El-Watch can provide everything needed to get started, but it helps to be aware of how the process works and what it demands. Here are our best pieces of advice on preparing your company for predictive maintenance:
Five tips to prepare your company for predictive maintenance
- Invest in software that can accommodate predictive maintenance.
- Choose an IoT sensor platform with full access to all sensor data.
- Start slowly and grow according to your business capacity. The technology often needs to mature in your company.
- Start transforming with the sensors available on the commercial market.
- Set aside sufficient parts of your planned technical equipment investment budget for predictive maintenance.
Five tips for performing and utilizing predictive maintenance in your company
- Always use digital tools for all jobs, and avoid «ghost protocols» on paper.
- When maintenance is performed, always consider if sensor monitoring can automate upkeep notifications.
- Use the sensor data to understand the context; the fault might be a result of another component’s malfunction.
- Considering sensor needs in relation to risk and consequence, some data is more important than others.
- Keep your crew up to speed with training and regular contact with suppliers.
Top 8 benefits of predictive maintenance
To sum up, predictive maintenance uses IoT, wireless sensors, and cloud-based business management to minimize upkeep expenses. It has proven beneficial in many industries and might help your company unlock significant production savings.
These are the top benefits you’ll gain:
- Lower maintenance costs
- Higher equipment performance
- Longer equipment lifespan
- More efficient workforce
- Safer work environment
- Efficient business management
- Better use of resources
- Financial stability
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Bloomberg: Predictive Maintenance Market worth $15.9 billion by 2026 – Exclusive Report by Markets and Markets™
Cornell University: A Survey of Predictive Maintenance: Systems, Purposes, and Approaches
Fiix: 5 causes of equipment failure (and how to prevent them)
Infraspeak: Is Predictive Maintenance Really Cost-Effective?
IoT Analytics: Predictive Maintenance Market: The Evolution from Niche Topic to High ROI Application
Harvard Business School: Digital Transformation: A New Roadmap for Success
Journal of Industrial Information Integration: Industrial Internet of Things monitoring solution for advanced predictive maintenance applications
Omdec: A History of CBM (Condition-Based Maintenance)
Prometheus Group: 11 Disadvantages of a Reactive Maintenance Program
Savvy Aviation: The Waddington Effect